RESUMO
Within post-conflict communities, attempts to identify and repatriate unidentified and missing individuals poses a difficult task. As current forensic strategies commonly lack the capacity to provide region of origin assessments, forensic anthropologists/investigators are often unable to identify sources of DNA for kinship analysis. Using Thermal Ionization Mass Spectrometry (TIMS), hair samples from 10 volunteers were used to assess the variation in strontium isotope ratios (87 Sr/86 Sr) between extant people in Guatemala City and Coban; with a leach (external) and digest (dietary) signal analyzed for each sample. A two-way anova demonstrated that the difference between 87 Sr/86 Sr of Guatemala City and Coban was statistically significant (F [1, 16] = 259.839, p < 0.05), with no statistically significant differences observed between leach and digest 87 Sr/86 Sr (F [1,16] = 4.319, p = 0.054). Overall, individuals from Coban demonstrate 87 Sr/86 Sr comparable to previously recorded baseline values, demonstrating a minimal change in diet which is reflected in associated surveys. Volunteers from Guatemala City, however, show a marked shift in 87 Sr/86 Sr away from predicted values highlighting the potential influence of imported goods. The results here highlight the applicability of 87 Sr/86 Sr in hair to serve as a potential tool to support the identification of unknown individuals in Guatemala in a forensic context.
Assuntos
Monensin , Isótopos de Estrôncio , Cidades , Cabelo/química , Humanos , Espectrometria de Massas/métodos , Monensin/análise , Estrôncio/análise , Isótopos de Estrôncio/análiseRESUMO
This study tests whether postcranial sex estimation methods generated from Hispanic, and mainly Mexican samples, can be successfully applied to other increasingly common migrant populations from Central America. We use a sample of postcranial data from a modern (1980s) Guatemalan Maya sample (n = 219). Results indicate a decrease in classification accuracies for previously established univariate methods when applied to the Guatemalan study sample, specifically for males whose accuracies ranged from 30 to 84%. This bias toward inaccuracies for Guatemalan males is associated with the smaller skeletal sizes for the Guatemalan sample as compared to the samples used in the tested sex estimation methods. In contrast, the tested multivariate discriminant function classification yielded less sex bias and improved classification accuracies ranging from 82 to 89%. Our results highlight which of the tested univariate and multivariate methods reach acceptable levels for accuracy for sex estimation of cases where the region of origin may include Guatemala.